Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data

Abstract

Background: Estimates of the size of the undiagnosed HIV-infected population are important to understand the HIV epidemic and to plan interventions, including "test-and-treat" strategies.

Methods: We developed a multi-state back-calculation model to estimate HIV incidence, time between infection and diagnosis, and the undiagnosed population by CD4 count strata, using surveillance data on new HIV and AIDS diagnoses. The HIV incidence curve was modelled using cubic splines. The model was tested on simulated data and applied to surveillance data on men who have sex with men in The Netherlands.

Results: The number of HIV infections could be estimated accurately using simulated data, with most values within the 95% confidence intervals of model predictions. When applying the model to Dutch surveillance data, 15,400 (95% confidence interval [CI] = 15,000, 16,000) men who have sex with men were estimated to have been infected between 1980 and 2011. HIV incidence showed a bimodal distribution, with peaks around 1985 and 2005 and a decline in recent years. Mean time to diagnosis was 6.1 (95% CI = 5.8, 6.4) years between 1984 and 1995 and decreased to 2.6 (2.3, 3.0) years in 2011. By the end of 2011, 11,500 (11,000, 12,000) men who have sex with men in The Netherlands were estimated to be living with HIV, of whom 1,750 (1,450, 2,200) were still undiagnosed. Of the undiagnosed men who have sex with men, 29% (22, 37) were infected for less than 1 year, and 16% (13, 20) for more than 5 years.

Conclusions: This multi-state back-calculation model will be useful to estimate HIV incidence, time to diagnosis, and the undiagnosed HIV epidemic based on routine surveillance data.

FIGURE 1.

Simplified model structure. HIV incidence over calendar time t is denoted by I(t). Immediately after infection, all individuals first enter a phase of primary infection. After primary infection, individuals enter at a rate one of four AIDS-free CD4 compartments of undiagnosed HIV infection with . We assume that no HIV-infected individuals immediately progress to AIDS after primary infection. In the absence of treatment, individuals progress to the next compartment at a rate q_i(i=1,…,5) until they develop AIDS and then die because of AIDS at a rate q₅. During each stage except primary infection individuals can be diagnosed at a rate d_i(t), depending on the stage and on calendar time.

FIGURE 2.

Estimated and true number of infections for three different simulated HIV epidemics. Black solid lines show the model estimates, and dashed lines are 95% confidence intervals. Thin grey lines show results of multivariable sensitivity analyses. Grey dots are the true annual number of infections.

FIGURE 3.

Estimated and true number of undiagnosed infections for three different simulated HIV epidemics. Black solid lines show the model estimates, and dashed lines are 95% confidence intervals. Thin grey lines show results of multivariable sensitivity analyses. Grey dots are the true annual number of undiagnosed infections.

FIGURE 4.

Model outcomes for men who have sex with men (MSM) in The Netherlands. A, Annual number of new HIV infections; (B) average time from HIV infection to diagnosis by year of infection if diagnosis rates would remain the same as in the year of infection; (C) average time from HIV infection to diagnosis by year of diagnosis; (D) total number of individuals living with HIV and number of diagnosed and undiagnosed HIV infections, with dots representing the number of diagnosed MSM living with HIV according to the ATHENA database. Thin grey lines show results of multivariable sensitivity analyses.